Accurately tracking the position of xr hmd is critical for maintaining high user qoe. Modern hmd achieve this using precise visible/ir light tracking equipment. Unfortunately, visual/ir tracking is also prone to outage in adverse circumstances. Although an hmd can switch to imu based tracking in such situations, long term visual/ir outage will result in error buildup due to accelerometer and gyroscope drift integration.
hmd mostly rely on two major visual/ir tracking techniques: a) the hmd gathers visual/ir information from an illuminated environment
or b) the hmd receives visual/ir signals from multiple external beacons in its vicinity. Examples of hmd fitted with a) and b) tracking options are the Oculus Quest 2 and the HTC Vive 2, respectively. Option a) is inherently prone to accuracy variations and outage due to limited control over environment illumination. Option b) offers precise tracking when the beacons lie within the hmd's field of view; yet, it experiences outage as soon as the hmd looses sight of the external beacons [1]}. A possible cause is either the user waving their hand or another object passing between a beacon and the hmd. Moreover, the xr users can rotate their head away from the IR light sources. Outage can befall hmd equipped with option a) tracking in a similar manner when the main light source is blocked. Hence, manufacturers should foresee visual/ir tracking outage and provide the hmd with alternative tracking solutions.
To avoid error-prone hmd-based tracking during longer visual/ir tracking outage, the wireless communication transceivers in an hmd can double as a sensing device. The transceiver provides directly the hmd orientation through aoa estimation, while velocity is extracted from Doppler frequency shift estimation; thus, removing the need for one numerical integration step in both cases, compared to imu tracking. Sensing the environment by estimating the aoa, toa, or Doppler frequency extracted from communication signals, called *jcas has been proposed used in recent literature [2]}, [3]}, [4]}, [5]}.
In the work at hand, we propose and evaluate a Doppler frequency and AoA technique for minimizing xr hmd tracking error during visual/ir tracking outage, called Dopamine. The proposed approach can be used alongside imu-based tracking and substitute it dynamically upon error accumulation. Dopamine is primarily designed to operate in the 60 GHz mmwave frequency band and loosely conform with conventions standardized in IEEE 802.11ad/ay. Moreover, Dopamine's flexibility allows for easy portability to other frequency bands and standards. Our contributions are three-fold: I) the extraction of hmd tracking data using limited wireless channel information, II) the analysis of tracking performance when employing either multiple ap or multiple ris, and III) the evaluation of the proposed tracking technique against imu-based tracking.
